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Modelling cheetah relocation success in southern Africa using an Iterative Bayesian Network Development Cycle

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  • Johnson, Sandra
  • Mengersen, Kerrie
  • de Waal, Alta
  • Marnewick, Kelly
  • Cilliers, Deon
  • Houser, Ann Marie
  • Boast, Lorraine

Abstract

Relocation is one of the strategies used by conservationists to deal with problem cheetahs in southern Africa. The success of a relocation event and the factors that influence it within the broader context of long-term viability of wild cheetah metapopulations was the focus of a Bayesian Network (BN) modelling workshop in South Africa. Using a new heuristics, Iterative Bayesian Network Development Cycle (IBNDC), described in this paper, several networks were formulated to distinguish between the unique relocation experiences and conditions in Botswana and South Africa. There were many common underlying factors, despite the disparate relocation strategies and sites in the two countries. The benefit of relocation BNs goes beyond the identification and quantification of the factors influencing the success of relocations and population viability. They equip conservationists with a powerful communication tool in their negotiations with land and livestock owners, which is key to the long-term survival of cheetahs in southern Africa. Importantly, the IBNDC provides the ecological modeller with a methodological process that combines several BN design frameworks to facilitate the development of a BN in a multi-expert and multi-field domain.

Suggested Citation

  • Johnson, Sandra & Mengersen, Kerrie & de Waal, Alta & Marnewick, Kelly & Cilliers, Deon & Houser, Ann Marie & Boast, Lorraine, 2010. "Modelling cheetah relocation success in southern Africa using an Iterative Bayesian Network Development Cycle," Ecological Modelling, Elsevier, vol. 221(4), pages 641-651.
    • Handle: RePEc:eee:ecomod:v:221:y:2010:i:4:p:641-651
    DOI: 10.1016/j.ecolmodel.2009.11.012
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    References listed on IDEAS

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    1. Pollino, Carmel A. & White, Andrea K. & Hart, Barry T., 2007. "Examination of conflicts and improved strategies for the management of an endangered Eucalypt species using Bayesian networks," Ecological Modelling, Elsevier, vol. 201(1), pages 37-59.
    2. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    3. Sarah M. Durant & Marcella Kelly & Tim M. Caro, 2004. "Factors affecting life and death in Serengeti cheetahs: environment, age, and sociality," Behavioral Ecology, International Society for Behavioral Ecology, vol. 15(1), pages 11-22, January.
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    2. Guo, Kai & Zhang, Xinchang & Kuai, Xi & Wu, Zhifeng & Chen, Yiyun & Liu, Yi, 2020. "A spatial bayesian-network approach as a decision-making tool for ecological-risk prevention in land ecosystems," Ecological Modelling, Elsevier, vol. 419(C).
    3. Stewart-Koster, Ben & Dieu Anh, Nguyen & Burford, Michele A. & Condon, Jason & Qui, Nguyen Van & Hiep, Le Huu & Bay, Doan Van & Sammut, Jesmond, 2017. "Expert based model building to quantify risk factors in a combined aquaculture-agriculture system," Agricultural Systems, Elsevier, vol. 157(C), pages 230-240.
    4. Liedloff, Adam C. & Smith, Carl S., 2010. "Predicting a ‘tree change’ in Australia's tropical savannas: Combining different types of models to understand complex ecosystem behaviour," Ecological Modelling, Elsevier, vol. 221(21), pages 2565-2575.
    5. Florian J Weise & Ken J Stratford & Rudolf J van Vuuren, 2014. "Financial Costs of Large Carnivore Translocations – Accounting for Conservation," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-10, August.
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